import cv2import mediapipe as mpimport pandas as pdfrom sklearnneighbors import KNeighborsClassifierimport os# 初始化MediaPipe的人体姿势模型mp_drawing = mpsolutionsdrawing_utilsmp_pose = mpsolutionspose# 打开输入视频

可以将KNN分类器替换为其他分类算法，例如决策树、随机森林、支持向量机等。只需要将KNN分类器的代码替换为其他分类算法的代码即可。例如，使用决策树分类器可以这样实现：

from sklearn.tree import DecisionTreeClassifier

# 训练决策树分类器
tree = DecisionTreeClassifier()
tree.fit(data.iloc[:, :-1], data['label'])

# 处理视频文件中的每一帧
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
    while cap.isOpened():
        # 读取一帧
        ret, frame = cap.read()
        if not ret:
            break

        # 将帧转换为RGB格式
        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

        # 处理人体姿势检测
        results = pose.process(image)

        # 判断是否检测到人体
        if results.pose_landmarks:
            # 绘制人体姿势
            mp_drawing.draw_landmarks(
                frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)

            # 获取人体姿势信息
            pose_data = []
            keypoints = [0, 11, 12, 13, 14, 15, 16, 23, 24, 25, 26, 27, 28]  # 需要的关键点的索引
            for i in keypoints:
                landmark = results.pose_landmarks.landmark[i]
                if landmark.visibility < 0.5:  # 如果关键点可见度小于0.5，则跳过
                    continue
                pose_data.extend([landmark.x, landmark.y])

            # 将姿势信息输入决策树分类器进行预测
            label = tree.predict([pose_data])

            # 在输出图片上显示动作类型
            cv2.putText(frame, label[0], (5, 60),
                        cv2.FONT_HERSHEY_SIMPLEX, 1.1, (255, 100, 100), 2)

        else:
            # 如果未检测到人体，则跳过本帧处理
            cv2.putText(frame, "No body detected", (50, 50),
                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

        # 将帧写入输出视频文件
        out.write(frame)

        # 显示当前帧的结果
        cv2.imshow('MediaPipe Pose Detection press q exit', frame)

        # 检测是否按下q键退出
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

# 释放资源
cap.release()
out.release()
cv2.destroyAllWindows()
``